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EP1971167A1 - Verfahren und Vorrichtung zur Koordination von Kommunikationsressourcen, entsprechendes Kommunikationssystem und Computerprogrammprodukt - Google Patents

Verfahren und Vorrichtung zur Koordination von Kommunikationsressourcen, entsprechendes Kommunikationssystem und Computerprogrammprodukt Download PDF

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Publication number
EP1971167A1
EP1971167A1 EP07425145A EP07425145A EP1971167A1 EP 1971167 A1 EP1971167 A1 EP 1971167A1 EP 07425145 A EP07425145 A EP 07425145A EP 07425145 A EP07425145 A EP 07425145A EP 1971167 A1 EP1971167 A1 EP 1971167A1
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EP
European Patent Office
Prior art keywords
channel resources
function
parameter
channel
users
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
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Application number
EP07425145A
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English (en)
French (fr)
Inventor
Stefano Sorrentino
Umberto Spagnolini
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Politecnico di Milano
Nokia Solutions and Networks SpA
Original Assignee
Politecnico di Milano
Siemens Networks SpA
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Publication date
Application filed by Politecnico di Milano, Siemens Networks SpA filed Critical Politecnico di Milano
Priority to EP07425145A priority Critical patent/EP1971167A1/de
Priority to PCT/EP2008/052881 priority patent/WO2008110556A1/en
Publication of EP1971167A1 publication Critical patent/EP1971167A1/de
Withdrawn legal-status Critical Current

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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/50—Allocation or scheduling criteria for wireless resources
    • H04W72/54—Allocation or scheduling criteria for wireless resources based on quality criteria
    • H04W72/542—Allocation or scheduling criteria for wireless resources based on quality criteria using measured or perceived quality

Definitions

  • the invention relates to techniques for scheduling (i.e. selectively allocating) channel resources in wireless communications.
  • the task of scheduling channel resources in communications systems and networks is performed by modules currently referred to as "schedulers".
  • the objective of schedulers is to assign channel resources (e.g. channel assignment to users, bandwidth, spatial beams, etc) in a smart way.
  • the objective is to maximize the throughput (e.g. the number of served users) while still complying with Quality of Service (QoS) constraints.
  • QoS Quality of Service
  • So-called "channel aware" schedulers play a crucial role in the performance enhancement of wireless systems (such as WiMAX 802.16x, 3GPP LTE - HSDPA, Winner, etc) by taking advantage of channel fluctuations for leveraging the gain that results in user selections (multiuser diversity) to maximize the throughput performance.
  • wireless systems such as WiMAX 802.16x, 3GPP LTE - HSDPA, Winner, etc
  • Schedulers such as the so-called "opportunistic" schedulers take advantage of users' channel fluctuations in order to leverage multiuser throughput gain by adaptively allocating the contended resource to the users that experience the best channel quality.
  • Opportunistic schedulers achieve a convenient trade-off between sum-rate and fairness by optimizing the resource allocation with respect to the channel performance over past assignments (e.g., past user channel quality measurements or user throughput).
  • QoS Quality of Service
  • the Proportional Fair Scheduler (PFS) [1] is based on past measurements of the channel quality for the scheduling optimization in the current time-slot.
  • recent advances in channel power prediction e.g., see [2] and references therein
  • CDF Cumulative Density Function
  • scheduler e.g., see [7-9]
  • asymptotically i.e. for a large number of users
  • throughput optimality and long-term fairness among users [9] that hold true even when different users experience heterogeneous fading statistics [7].
  • the idea underlying a CDF scheduler is to opportunistically select those users that experience the best channel according to their cumulative function of the channel distribution that can be empirically estimated e.g. from the past channel measurements [7].
  • the object of the invention is thus to meet the interest as previously specified.
  • the invention also relates to corresponding apparatus (i.e. a scheduler), a communication system including such as scheduler, as well as a related computer program product, loadable in the memory of at least one computer and including software code portions for performing the steps of the method of the invention when the product is run on a computer.
  • a scheduler i.e. a communication system including such as scheduler, as well as a related computer program product, loadable in the memory of at least one computer and including software code portions for performing the steps of the method of the invention when the product is run on a computer.
  • a computer program product is intended to be equivalent to reference to a computer-readable medium containing instructions for controlling a computer system to coordinate the performance of the method of the invention.
  • Reference to "at least one computer” is evidently intended to highlight the possibility for the present invention to be implemented in a distributed/ modular fashion.
  • a preferred embodiment of the arrangement described herein is thus a method of allocating channel resources to users of a wireless communication system, the method including the steps of:
  • said respective allocation scores are allotted as a causal/anticausal function of past measurements of said at least one parameter of said channel resources expressed in a closed mathematical form, i.e. including weights expressed in a closed non-iterative form.
  • the parameter in question may be selected e.g. out of a Signal-to-Noise Ratio and an interference level and/or combinations thereof.
  • the arrangement described herein provides an opportunistic scheduler based on cumulative density function with the introduction of predicted channel quality values.
  • the Predictive Score Based Scheduler (Predictive-SBS) arrangement described herein is thus computationally convenient and achieves a better trade-off between sum-rate and fairness when compared to non-predictive schedulers.
  • the arrangement described herein involves a non-iterative scheme that optimally takes into account prediction uncertainty.
  • the arrangement described herein provides a framework for the optimization of the CDF based scheduler by taking advantage of both past and future (i.e., in terms of predicted) channel values; the corresponding procedure as described is robust with respect to the unreliable channel predictions since the scheduler is made aware of the prediction error power; furthermore, such a procedure lends itself to an efficient implementation due to its non-iterative nature.
  • MIMO Multiple Input Multiple Output
  • OFDM Orthogonal Frequency Division Multiplexing
  • OFDMA Orthogonal Frequency Division Multiple Access
  • the block diagram of figure 1 is representative of a wireless network such as e.g. a cellular communication network and/or a network of any of the exemplary types mentioned in the introductory portion of this description.
  • the network of figure 1 includes a Base Station BS cooperating with a number K of Mobile Stations MS1, MS2, ... , MSk.
  • the impulse responses of the single antenna channels h k (t) will thus be represented as stationary random processes with mean value ⁇ k (generally, a complex number) and variance ⁇ k 2 .
  • the channels h k (t) are arbitrarily correlated in time and mutually uncorrelated among different users.
  • ⁇ k ( t )
  • 2 be the SNR (Signal-to-Noise Ratio) value for the k-th user in time slot t (unitary noise power is assumed).
  • the score ⁇ k ( t ) is regarded as the relative quality of the current SNR ⁇ k ( t ) for the user k compared to its empirical distribution over the scheduling time window N w . Therefore, the objective of the Score Base Scheduler is to select - as a function of a channel characteristic represented here by the current SNR ⁇ k ( t ) - those users that experience a good channel realization according to their own channel statistical distribution and compared with the percentile of the others.
  • the score function for the scheduling will be based on the prediction of the SNR ⁇ k ( t+ ⁇ ) based on the knowledge of the channel for ⁇ 0.
  • the Base Station BS will include a Predictive Score Based Scheduler P-SBS.
  • the score ((4) cannot be evaluated in real time implementation as the future SNR samples ⁇ k ( t+ ⁇ ) for the evaluation of ⁇ ⁇ k t are not available yet.
  • two different approaches are considered herein for the evaluation of the anti-causal term in ((4) based on the prediction values ⁇ k ( t+ ⁇ ) (Predictive-SBS, or P-SBS, Type 1) or the direct estimation of (4) (Predictive-SBS, or P-SBS, Type 2).
  • Eq. 5 defines the P-SBS Type 1.
  • the way to optimally estimate the future SNR values ⁇ k ( t + ⁇ ) will be discussed in greater detail in the following by referring to the exemplary case of an unbiased estimator.
  • the P-SBS Type 2 is optimal with regards to the prediction error, as Eq.
  • 6) is the Minimum Mean Squared Error [10] estimator of the score ⁇ k ( t ) in ((4) conditioned to the knowledge of the faded channels over time.
  • the closed form solution for 6) is based on specific assumptions on fading as it will be shown in the following for the optimal linear channel predictor.
  • h k t ⁇ ⁇ h , k 2 ⁇ t + ⁇
  • ⁇ ⁇ h , k 2 ⁇ t + ⁇
  • 2 ⁇ k 2 - c k H ⁇ ⁇ R k - 1 ⁇ c k ⁇ is the power of the channel prediction error.
  • conditional density for the future channel values is [10]: h k ⁇ t + ⁇ ⁇ CN ⁇ h ⁇ k ( t + ⁇ ) , ⁇ ⁇ h , k 2 ⁇ t + ⁇ , where ⁇ k ( t + ⁇ ) is given by (8) and ⁇ ⁇ h , k 2 ⁇ t + ⁇ is given by (12).
  • the P-SBS Type 2 has a simple implementation that does not need any suboptimal iterative optimization as required for other predictive schedulers [4].
  • Performance of the P-SBS described herein has been quite favourably compared with performance of standard Score Base Scheduler and PFS schedulers.
  • the i.i.d. (independent identically distributed) unit power channels are temporally correlated according to their moving speed, these are generated as autoregressive processes with 32 poles [14] obtained by solving the Yule-Walker equations for the band-limited channels with Jakes' correlation.
  • Unbiased SNR prediction is performed with 32 samples length MMSE power predictor (13), as for the channel stationarity the linear prediction shows to be optimal.
  • the max SNR scheduler k * t arg max k ⁇ k t was also considered, which maximizes the sum-rate but disregards the fairness constraints. Therefore, the max SNR scheduler (21) achieves an upper bound of the sum-rate.
  • the sum-rate performance of the scheduler herein in a Non-LoS scenario shows that that the Predictive-SBS Type 2 achieves a significant sum-rate gain with respects to the Score Based Scheduler of [7] and the P-SBS Type 1.
  • the relative sum-rate gain provided by the P-SBS Type 2 towards the Score Based Scheduler increases with the number of users K.
  • the Predictive-SBS Type 2 thus provides a better trade-off between sum-rate and fairness than the Score Based Scheduler [7], in the considered scenario. Also, the Predictive-SBS Type 2 was found to achieve a higher sum-rate than the Predictive-SBS Type 1, which bears witness to the possibility of achieving an appreciable performance boost by optimally taking into account the prediction uncertainty.
  • a similar scenario has already been evaluated in [7], in order to compare the Score Based Scheduler and the PFS.
  • PFS becomes unfair in this scenario; conversely, the Score Based Scheduler and the Predictive-SBS described herein were found to still achieve fairness in the scheduling probability of different users.
  • the scheduling is optimal, provided that the channel coherence time is smaller than the scheduling window N w .
  • the scheduler is provided with additional fairness enforcement controls in order to prevent users to stack on favourable (and slowly varying) channel conditions.
  • the results prove that the predictive scheduler described herein improves trade-off between sum-rate and fairness with respects to non predictive schedulers with a computationally economical implementation.

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  • Engineering & Computer Science (AREA)
  • Quality & Reliability (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Mobile Radio Communication Systems (AREA)
EP07425145A 2007-03-15 2007-03-15 Verfahren und Vorrichtung zur Koordination von Kommunikationsressourcen, entsprechendes Kommunikationssystem und Computerprogrammprodukt Withdrawn EP1971167A1 (de)

Priority Applications (2)

Application Number Priority Date Filing Date Title
EP07425145A EP1971167A1 (de) 2007-03-15 2007-03-15 Verfahren und Vorrichtung zur Koordination von Kommunikationsressourcen, entsprechendes Kommunikationssystem und Computerprogrammprodukt
PCT/EP2008/052881 WO2008110556A1 (en) 2007-03-15 2008-03-11 Method and apparatus for scheduling communication resources, corresponding communication system and computer program product

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
EP07425145A EP1971167A1 (de) 2007-03-15 2007-03-15 Verfahren und Vorrichtung zur Koordination von Kommunikationsressourcen, entsprechendes Kommunikationssystem und Computerprogrammprodukt

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EP1971167A1 true EP1971167A1 (de) 2008-09-17

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2012169947A1 (en) * 2011-06-10 2012-12-13 Telefonaktiebolaget L M Ericsson (Publ) Closed control loop for uplink scheduling
EP2632223A1 (de) * 2012-02-24 2013-08-28 NTT DoCoMo, Inc. Vorrichtung und Verfahren zur Planung der Übertragung der von einer Basisstation bereitgestellten Ressourcen für Benutzer mit Vorhersage von Ratenbereichen

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5886988A (en) * 1996-10-23 1999-03-23 Arraycomm, Inc. Channel assignment and call admission control for spatial division multiple access communication systems
WO2000051390A1 (en) * 1999-02-22 2000-08-31 Telefonaktiebolaget Lm Ericsson (Publ) Mobile radio system and a method for channel allocation in a mobile radio system
US20050227697A1 (en) * 2004-04-12 2005-10-13 Lucent Technologies, Inc. Method and apparatus for channel prediction in wireless networks

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5886988A (en) * 1996-10-23 1999-03-23 Arraycomm, Inc. Channel assignment and call admission control for spatial division multiple access communication systems
WO2000051390A1 (en) * 1999-02-22 2000-08-31 Telefonaktiebolaget Lm Ericsson (Publ) Mobile radio system and a method for channel allocation in a mobile radio system
US20050227697A1 (en) * 2004-04-12 2005-10-13 Lucent Technologies, Inc. Method and apparatus for channel prediction in wireless networks

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2012169947A1 (en) * 2011-06-10 2012-12-13 Telefonaktiebolaget L M Ericsson (Publ) Closed control loop for uplink scheduling
US20140112281A1 (en) * 2011-06-10 2014-04-24 Telefonaktiebolaget L M Ericsson (Publ) Closed Control Loop for Uplink Scheduling
US9480078B2 (en) * 2011-06-10 2016-10-25 Telefonaktiebolaget Lm Ericsson (Publ) Closed control loop for uplink scheduling
EP2632223A1 (de) * 2012-02-24 2013-08-28 NTT DoCoMo, Inc. Vorrichtung und Verfahren zur Planung der Übertragung der von einer Basisstation bereitgestellten Ressourcen für Benutzer mit Vorhersage von Ratenbereichen
US9107235B2 (en) 2012-02-24 2015-08-11 Ntt Docomo, Inc. Apparatus and method for scheduling transmission resources to users served by a base station using a prediction of rate regions

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